{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 猫狗识别\n",
    "\n",
    "- 导入数据集\n",
    "- 数据预处理，准备验证验证集和测试集\n",
    "- 构建卷积网络模型\n",
    "- 观察验证集和验证集上的效果，针对过拟合问题，提出解决办法\n",
    "- 数据增强\n",
    "- 迁移学习，深度学习的必备策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入数据分析以及图像分类任务常用的工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定好数据的路径\n",
    "import os\n",
    "base_dir = 'D:\\image_dataset\\cats_and_dogs'\n",
    "train_dir = os.path.join(base_dir,'train')\n",
    "val_dir = os.path.join(base_dir,'validation')\n",
    "\n",
    "#训练集\n",
    "train_cats_dir = os.path.join(train_dir,'cats')\n",
    "train_dogs_dir = os.path.join(train_dir,'dogs')\n",
    "\n",
    "#验证集\n",
    "val_cats_dir = os.path.join(val_dir,'cats')\n",
    "val_dogs_dir = os.path.join(val_dir,'dogs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建卷积神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    #添加第一个卷积池化层\n",
    "    tf.keras.layers.Conv2D(32,(3,3),activation = 'relu',input_shape = (64,64,3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第二个卷积池化层\n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation = 'relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第三个卷积池化层\n",
    "    tf.keras.layers.Conv2D(128,(3,3),activation = 'relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    \n",
    "    #将卷积之后得到的feature_map展平为长向量\n",
    "    tf.keras.layers.Flatten(),\n",
    "    \n",
    "    #添加全连接层\n",
    "    tf.keras.layers.Dense(256,activation = 'relu'),\n",
    "    #添加输出层\n",
    "    tf.keras.layers.Dense(1,activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,273,409\n",
      "Trainable params: 1,273,409\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#打印模型的结构\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#配置模型\n",
    "\n",
    "model.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理\n",
    "\n",
    "- 读进来的数据会自动被转化为tensor(float32)格式，分别准备训练集和验证机\n",
    "- 图像数据归一化处理，将图像数据的像素值转化为(0-1)之间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1996 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#构建数据生成器\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "val_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                   target_size = (64,64),#指定resize的大小\n",
    "                                                   batch_size = 20,\n",
    "                                                   class_mode = 'binary')\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(val_dir,\n",
    "                                                   target_size = (64,64),\n",
    "                                                   batch_size = 20,\n",
    "                                                   class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "100/100 - 23s - loss: 0.6966 - acc: 0.5005 - val_loss: 0.6904 - val_acc: 0.5910\n",
      "Epoch 2/20\n",
      "100/100 - 20s - loss: 0.6834 - acc: 0.5842 - val_loss: 0.6485 - val_acc: 0.6400\n",
      "Epoch 3/20\n",
      "100/100 - 20s - loss: 0.6280 - acc: 0.6573 - val_loss: 0.5868 - val_acc: 0.6790\n",
      "Epoch 4/20\n",
      "100/100 - 18s - loss: 0.5655 - acc: 0.7124 - val_loss: 0.5932 - val_acc: 0.6860\n",
      "Epoch 5/20\n",
      "100/100 - 21s - loss: 0.5162 - acc: 0.7315 - val_loss: 0.5860 - val_acc: 0.7010\n",
      "Epoch 6/20\n",
      "100/100 - 21s - loss: 0.4817 - acc: 0.7700 - val_loss: 0.5785 - val_acc: 0.7020\n",
      "Epoch 7/20\n",
      "100/100 - 21s - loss: 0.4191 - acc: 0.8136 - val_loss: 0.5702 - val_acc: 0.7150\n",
      "Epoch 8/20\n",
      "100/100 - 21s - loss: 0.3324 - acc: 0.8572 - val_loss: 0.6191 - val_acc: 0.7200\n",
      "Epoch 9/20\n",
      "100/100 - 21s - loss: 0.2597 - acc: 0.8903 - val_loss: 0.6117 - val_acc: 0.7070\n",
      "Epoch 10/20\n",
      "100/100 - 20s - loss: 0.2035 - acc: 0.9193 - val_loss: 0.7065 - val_acc: 0.7290\n",
      "Epoch 11/20\n",
      "100/100 - 20s - loss: 0.1293 - acc: 0.9549 - val_loss: 0.7948 - val_acc: 0.7220\n",
      "Epoch 12/20\n",
      "100/100 - 20s - loss: 0.0946 - acc: 0.9669 - val_loss: 0.9560 - val_acc: 0.6970\n",
      "Epoch 13/20\n",
      "100/100 - 20s - loss: 0.0704 - acc: 0.9775 - val_loss: 1.1030 - val_acc: 0.7070\n",
      "Epoch 14/20\n",
      "100/100 - 20s - loss: 0.0519 - acc: 0.9855 - val_loss: 1.2072 - val_acc: 0.7210\n",
      "Epoch 15/20\n",
      "100/100 - 20s - loss: 0.0460 - acc: 0.9830 - val_loss: 1.2437 - val_acc: 0.7190\n",
      "Epoch 16/20\n",
      "100/100 - 20s - loss: 0.0883 - acc: 0.9629 - val_loss: 1.0410 - val_acc: 0.7120\n",
      "Epoch 17/20\n",
      "100/100 - 20s - loss: 0.0413 - acc: 0.9875 - val_loss: 1.2907 - val_acc: 0.7280\n",
      "Epoch 18/20\n",
      "100/100 - 19s - loss: 0.0096 - acc: 0.9990 - val_loss: 1.3679 - val_acc: 0.7340\n",
      "Epoch 19/20\n",
      "100/100 - 21s - loss: 0.0021 - acc: 1.0000 - val_loss: 1.5163 - val_acc: 0.7350\n",
      "Epoch 20/20\n",
      "100/100 - 20s - loss: 8.9166e-04 - acc: 1.0000 - val_loss: 1.5461 - val_acc: 0.7390\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 100,\n",
    "                   epochs = 20,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 50,\n",
    "                   verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上面的训练效果可以看出，该模型存在明显的过拟合现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型改进"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在卷积层和全连接层中加入正则化惩罚项"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1 = tf.keras.models.Sequential([\n",
    "    #添加第一个卷积池化层\n",
    "    tf.keras.layers.Conv2D(32,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03),\n",
    "                           input_shape = (64,64,3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第二个卷积池化层\n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第三个卷积池化层\n",
    "    tf.keras.layers.Conv2D(128,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    \n",
    "    #将卷积之后得到的feature_map展平为长向量\n",
    "    tf.keras.layers.Flatten(),\n",
    "    \n",
    "    #添加全连接层\n",
    "    tf.keras.layers.Dense(256,activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03)),\n",
    "    #添加输出层\n",
    "    tf.keras.layers.Dense(1,activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#配置该模型\n",
    "model_1.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "100/100 - 21s - loss: 3.0409 - acc: 0.5020 - val_loss: 0.7255 - val_acc: 0.5000\n",
      "Epoch 2/20\n",
      "100/100 - 21s - loss: 0.7030 - acc: 0.4920 - val_loss: 0.6949 - val_acc: 0.5000\n",
      "Epoch 3/20\n",
      "100/100 - 22s - loss: 0.6938 - acc: 0.5010 - val_loss: 0.6933 - val_acc: 0.5000\n",
      "Epoch 4/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 5/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4920 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 6/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4920 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 7/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4930 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 8/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 9/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4940 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 10/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 11/20\n",
      "100/100 - 22s - loss: 0.6932 - acc: 0.4870 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 12/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 13/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4880 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 14/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4880 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 15/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4810 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 16/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 17/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 18/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.4840 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 19/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 20/20\n",
      "100/100 - 22s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6931 - val_acc: 0.5000\n"
     ]
    }
   ],
   "source": [
    "#训练模型\n",
    "history = model_1.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 100,\n",
    "                   epochs = 20,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 50,\n",
    "                   verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由模型的训练结果来看，该模型始终无法收敛。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在卷积层和全连接层中加入高斯随机初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2 = tf.keras.models.Sequential([\n",
    "    #添加第一个卷积池化层\n",
    "    tf.keras.layers.Conv2D(32,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03),\n",
    "                           kernel_initializer = 'random_normal',input_shape = (64,64,3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第二个卷积池化层\n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03),\n",
    "                          kernel_initializer = 'random_normal'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    #添加第三个卷积池化层\n",
    "    tf.keras.layers.Conv2D(128,(3,3),activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03),\n",
    "                          kernel_initializer = 'random_normal'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    \n",
    "    \n",
    "    #将卷积之后得到的feature_map展平为长向量\n",
    "    tf.keras.layers.Flatten(),\n",
    "    \n",
    "    #添加全连接层\n",
    "    tf.keras.layers.Dense(256,activation = 'relu',kernel_regularizer = tf.keras.regularizers.l2(0.03),\n",
    "                         kernel_initializer = 'random_normal'),\n",
    "    #添加输出层\n",
    "    tf.keras.layers.Dense(1,activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "100/100 - 22s - loss: 28.9256 - acc: 0.4995 - val_loss: 4.1864 - val_acc: 0.5000\n",
      "Epoch 2/20\n",
      "100/100 - 21s - loss: 1.7701 - acc: 0.5010 - val_loss: 0.8470 - val_acc: 0.5000\n",
      "Epoch 3/20\n",
      "100/100 - 21s - loss: 0.7422 - acc: 0.5010 - val_loss: 0.7006 - val_acc: 0.5000\n",
      "Epoch 4/20\n",
      "100/100 - 21s - loss: 0.6956 - acc: 0.4770 - val_loss: 0.6935 - val_acc: 0.5000\n",
      "Epoch 5/20\n",
      "100/100 - 21s - loss: 0.6934 - acc: 0.4930 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 6/20\n",
      "100/100 - 21s - loss: 0.6933 - acc: 0.4860 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 7/20\n",
      "100/100 - 21s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 8/20\n",
      "100/100 - 12s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 9/20\n",
      "100/100 - 8s - loss: 0.6932 - acc: 0.4900 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 10/20\n",
      "100/100 - 8s - loss: 0.6932 - acc: 0.4820 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 11/20\n",
      "100/100 - 8s - loss: 0.6932 - acc: 0.4870 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 12/20\n",
      "100/100 - 8s - loss: 0.6932 - acc: 0.4770 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 13/20\n",
      "100/100 - 9s - loss: 0.6934 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 14/20\n",
      "100/100 - 9s - loss: 0.6933 - acc: 0.5010 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 15/20\n",
      "100/100 - 9s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6932 - val_acc: 0.5000\n",
      "Epoch 16/20\n",
      "100/100 - 9s - loss: 0.6932 - acc: 0.4920 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 17/20\n",
      "100/100 - 10s - loss: 0.6932 - acc: 0.4940 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 18/20\n",
      "100/100 - 10s - loss: 0.6932 - acc: 0.5010 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 19/20\n",
      "100/100 - 10s - loss: 0.6932 - acc: 0.4950 - val_loss: 0.6931 - val_acc: 0.5000\n",
      "Epoch 20/20\n",
      "100/100 - 10s - loss: 0.6932 - acc: 0.4930 - val_loss: 0.6931 - val_acc: 0.5000\n"
     ]
    }
   ],
   "source": [
    "history = model_2.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 100,\n",
    "                   epochs = 20,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 50,\n",
    "                   verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据增强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上两次的改进的效果都不好，为了提升模型的效果，最有的效果的改进是从数据开始。在此我们我们采用数据增强，对原有的数据进行变换，扩充数据量。我们在ImageGenerator中设置图像数据变换的参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1996 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#构建数据生成器\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                  rotation_range = 30,\n",
    "                                  width_shift_range = 0.2,\n",
    "                                  height_shift_range = 0.2,\n",
    "                                  shear_range = 0.2,\n",
    "                                  zoom_range = 1.5,\n",
    "                                  horizontal_flip = True,\n",
    "                                  vertical_flip = True)\n",
    "val_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                   target_size = (64,64),#指定resize的大小\n",
    "                                                   batch_size = 50,\n",
    "                                                   class_mode = 'binary')\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(val_dir,\n",
    "                                                   target_size = (64,64),\n",
    "                                                   batch_size = 50,\n",
    "                                                   class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "40/40 - 10s - loss: 0.9373 - acc: 0.5556 - val_loss: 0.6487 - val_acc: 0.6280\n",
      "Epoch 2/30\n",
      "40/40 - 10s - loss: 0.6766 - acc: 0.5676 - val_loss: 0.6359 - val_acc: 0.6160\n",
      "Epoch 3/30\n",
      "40/40 - 9s - loss: 0.6606 - acc: 0.5907 - val_loss: 0.5979 - val_acc: 0.6680\n",
      "Epoch 4/30\n",
      "40/40 - 10s - loss: 0.6483 - acc: 0.6132 - val_loss: 0.6318 - val_acc: 0.6110\n",
      "Epoch 5/30\n",
      "40/40 - 10s - loss: 0.6409 - acc: 0.6197 - val_loss: 0.5918 - val_acc: 0.6870\n",
      "Epoch 6/30\n",
      "40/40 - 10s - loss: 0.6421 - acc: 0.6172 - val_loss: 0.5917 - val_acc: 0.6710\n",
      "Epoch 7/30\n",
      "40/40 - 10s - loss: 0.6286 - acc: 0.6328 - val_loss: 0.5954 - val_acc: 0.6760\n",
      "Epoch 8/30\n",
      "40/40 - 10s - loss: 0.6312 - acc: 0.6348 - val_loss: 0.5828 - val_acc: 0.6860\n",
      "Epoch 9/30\n",
      "40/40 - 10s - loss: 0.6262 - acc: 0.6408 - val_loss: 0.5953 - val_acc: 0.6750\n",
      "Epoch 10/30\n",
      "40/40 - 12s - loss: 0.6246 - acc: 0.6473 - val_loss: 0.5880 - val_acc: 0.6710\n",
      "Epoch 11/30\n",
      "40/40 - 12s - loss: 0.6268 - acc: 0.6463 - val_loss: 0.5812 - val_acc: 0.7010\n",
      "Epoch 12/30\n",
      "40/40 - 12s - loss: 0.6088 - acc: 0.6548 - val_loss: 0.5993 - val_acc: 0.6490\n",
      "Epoch 13/30\n",
      "40/40 - 12s - loss: 0.6236 - acc: 0.6488 - val_loss: 0.5854 - val_acc: 0.6830\n",
      "Epoch 14/30\n",
      "40/40 - 11s - loss: 0.6274 - acc: 0.6493 - val_loss: 0.6123 - val_acc: 0.6440\n",
      "Epoch 15/30\n",
      "40/40 - 12s - loss: 0.6197 - acc: 0.6503 - val_loss: 0.5862 - val_acc: 0.6860\n",
      "Epoch 16/30\n",
      "40/40 - 12s - loss: 0.6119 - acc: 0.6633 - val_loss: 0.5641 - val_acc: 0.7070\n",
      "Epoch 17/30\n",
      "40/40 - 12s - loss: 0.6139 - acc: 0.6488 - val_loss: 0.5745 - val_acc: 0.6990\n",
      "Epoch 18/30\n",
      "40/40 - 12s - loss: 0.6232 - acc: 0.6458 - val_loss: 0.5771 - val_acc: 0.6990\n",
      "Epoch 19/30\n",
      "40/40 - 11s - loss: 0.6182 - acc: 0.6518 - val_loss: 0.5639 - val_acc: 0.7030\n",
      "Epoch 20/30\n",
      "40/40 - 12s - loss: 0.6068 - acc: 0.6603 - val_loss: 0.5688 - val_acc: 0.6920\n",
      "Epoch 21/30\n",
      "40/40 - 11s - loss: 0.5992 - acc: 0.6643 - val_loss: 0.5744 - val_acc: 0.6820\n",
      "Epoch 22/30\n",
      "40/40 - 11s - loss: 0.6265 - acc: 0.6608 - val_loss: 0.5678 - val_acc: 0.6960\n",
      "Epoch 23/30\n",
      "40/40 - 11s - loss: 0.6085 - acc: 0.6523 - val_loss: 0.5835 - val_acc: 0.6840\n",
      "Epoch 24/30\n",
      "40/40 - 11s - loss: 0.6014 - acc: 0.6743 - val_loss: 0.5651 - val_acc: 0.6970\n",
      "Epoch 25/30\n",
      "40/40 - 11s - loss: 0.6000 - acc: 0.6543 - val_loss: 0.5785 - val_acc: 0.6890\n",
      "Epoch 26/30\n",
      "40/40 - 12s - loss: 0.6190 - acc: 0.6448 - val_loss: 0.5948 - val_acc: 0.6730\n",
      "Epoch 27/30\n",
      "40/40 - 11s - loss: 0.6185 - acc: 0.6418 - val_loss: 0.5679 - val_acc: 0.7010\n",
      "Epoch 28/30\n",
      "40/40 - 11s - loss: 0.6184 - acc: 0.6463 - val_loss: 0.5702 - val_acc: 0.7010\n",
      "Epoch 29/30\n",
      "40/40 - 11s - loss: 0.6023 - acc: 0.6663 - val_loss: 0.5854 - val_acc: 0.6870\n",
      "Epoch 30/30\n",
      "40/40 - 11s - loss: 0.5983 - acc: 0.6633 - val_loss: 0.5876 - val_acc: 0.6800\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 40,\n",
    "                   epochs = 30,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 20,\n",
    "                   verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图可以看出，模型未发生过拟合，在验证集上的准确率为68%。说明数据增强对于改善模型效果有着最直接的影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在卷积层和全连接层中加入Dropout层，观察模型效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    #添加第一个卷积池化层\n",
    "    tf.keras.layers.Conv2D(32,(3,3),activation = 'relu',input_shape = (64,64,3)),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    \n",
    "    #添加第二个卷积池化层\n",
    "    tf.keras.layers.Conv2D(64,(3,3),activation = 'relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    \n",
    "    #添加第三个卷积池化层\n",
    "    tf.keras.layers.Conv2D(128,(3,3),activation = 'relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    \n",
    "    \n",
    "    #将卷积之后得到的feature_map展平为长向量\n",
    "    tf.keras.layers.Flatten(),\n",
    "    \n",
    "    #添加全连接层\n",
    "    tf.keras.layers.Dense(256,activation = 'relu'),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    #添加输出层\n",
    "    tf.keras.layers.Dense(1,activation = 'sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_12 (Conv2D)           (None, 62, 62, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 31, 31, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 29, 29, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 12, 12, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 6, 6, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 256)               1179904   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 1,273,409\n",
      "Trainable params: 1,273,409\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss = 'binary_crossentropy',optimizer = 'adam', metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "40/40 - 11s - loss: 0.7085 - acc: 0.5105 - val_loss: 0.6922 - val_acc: 0.5000\n",
      "Epoch 2/30\n",
      "40/40 - 11s - loss: 0.6933 - acc: 0.4985 - val_loss: 0.6910 - val_acc: 0.5000\n",
      "Epoch 3/30\n",
      "40/40 - 10s - loss: 0.6940 - acc: 0.4935 - val_loss: 0.6926 - val_acc: 0.5000\n",
      "Epoch 4/30\n",
      "40/40 - 10s - loss: 0.6936 - acc: 0.4925 - val_loss: 0.6927 - val_acc: 0.5060\n",
      "Epoch 5/30\n",
      "40/40 - 10s - loss: 0.6921 - acc: 0.5025 - val_loss: 0.6933 - val_acc: 0.5000\n",
      "Epoch 6/30\n",
      "40/40 - 11s - loss: 0.6927 - acc: 0.5276 - val_loss: 0.6894 - val_acc: 0.5570\n",
      "Epoch 7/30\n",
      "40/40 - 11s - loss: 0.6925 - acc: 0.5195 - val_loss: 0.6913 - val_acc: 0.5000\n",
      "Epoch 8/30\n",
      "40/40 - 12s - loss: 0.6912 - acc: 0.5100 - val_loss: 0.6921 - val_acc: 0.5140\n",
      "Epoch 9/30\n",
      "40/40 - 13s - loss: 0.6913 - acc: 0.5185 - val_loss: 0.6875 - val_acc: 0.5050\n",
      "Epoch 10/30\n",
      "40/40 - 12s - loss: 0.6913 - acc: 0.5306 - val_loss: 0.6903 - val_acc: 0.5540\n",
      "Epoch 11/30\n",
      "40/40 - 12s - loss: 0.6926 - acc: 0.5145 - val_loss: 0.6932 - val_acc: 0.5010\n",
      "Epoch 12/30\n",
      "40/40 - 12s - loss: 0.6924 - acc: 0.5205 - val_loss: 0.6922 - val_acc: 0.5190\n",
      "Epoch 13/30\n",
      "40/40 - 13s - loss: 0.6883 - acc: 0.5511 - val_loss: 0.6862 - val_acc: 0.5490\n",
      "Epoch 14/30\n",
      "40/40 - 14s - loss: 0.6843 - acc: 0.5451 - val_loss: 0.6642 - val_acc: 0.5810\n",
      "Epoch 15/30\n",
      "40/40 - 13s - loss: 0.6894 - acc: 0.5356 - val_loss: 0.6855 - val_acc: 0.5580\n",
      "Epoch 16/30\n",
      "40/40 - 14s - loss: 0.6884 - acc: 0.5341 - val_loss: 0.6708 - val_acc: 0.5820\n",
      "Epoch 17/30\n",
      "40/40 - 13s - loss: 0.6805 - acc: 0.5556 - val_loss: 0.6778 - val_acc: 0.5530\n",
      "Epoch 18/30\n",
      "40/40 - 13s - loss: 0.6801 - acc: 0.5666 - val_loss: 0.6748 - val_acc: 0.5570\n",
      "Epoch 19/30\n",
      "40/40 - 14s - loss: 0.6818 - acc: 0.5566 - val_loss: 0.6732 - val_acc: 0.6050\n",
      "Epoch 20/30\n",
      "40/40 - 16s - loss: 0.6786 - acc: 0.5546 - val_loss: 0.6989 - val_acc: 0.5320\n",
      "Epoch 21/30\n",
      "40/40 - 15s - loss: 0.6795 - acc: 0.5541 - val_loss: 0.6645 - val_acc: 0.5730\n",
      "Epoch 22/30\n",
      "40/40 - 16s - loss: 0.6811 - acc: 0.5631 - val_loss: 0.6700 - val_acc: 0.6080\n",
      "Epoch 23/30\n",
      "40/40 - 16s - loss: 0.6716 - acc: 0.5782 - val_loss: 0.6538 - val_acc: 0.5960\n",
      "Epoch 24/30\n",
      "40/40 - 15s - loss: 0.6577 - acc: 0.5842 - val_loss: 0.6831 - val_acc: 0.5540\n",
      "Epoch 25/30\n",
      "40/40 - 15s - loss: 0.6665 - acc: 0.5792 - val_loss: 0.6501 - val_acc: 0.6190\n",
      "Epoch 26/30\n",
      "40/40 - 15s - loss: 0.6627 - acc: 0.5887 - val_loss: 0.6574 - val_acc: 0.5920\n",
      "Epoch 27/30\n",
      "40/40 - 15s - loss: 0.6523 - acc: 0.5962 - val_loss: 0.6475 - val_acc: 0.6090\n",
      "Epoch 28/30\n",
      "40/40 - 16s - loss: 0.6603 - acc: 0.5787 - val_loss: 0.6552 - val_acc: 0.5950\n",
      "Epoch 29/30\n",
      "40/40 - 16s - loss: 0.6565 - acc: 0.5952 - val_loss: 0.6456 - val_acc: 0.6190\n",
      "Epoch 30/30\n",
      "40/40 - 16s - loss: 0.6540 - acc: 0.6132 - val_loss: 0.6539 - val_acc: 0.5910\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 40,\n",
    "                   epochs = 30,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 20,\n",
    "                   verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 迁移学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.resnet import ResNet101\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.xception import Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_trained_model = Xception(\n",
    "    include_top=False, weights='imagenet', input_shape=(75,75,3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以选择哪些层训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in pred_trained_model.layers:\n",
    "    layer.trainable  = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入数据分析以及图像分类任务常用的工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定好数据的路径\n",
    "import os\n",
    "base_dir = 'D:\\image_dataset\\cats_and_dogs'\n",
    "train_dir = os.path.join(base_dir,'train')\n",
    "val_dir = os.path.join(base_dir,'validation')\n",
    "\n",
    "#训练集\n",
    "train_cats_dir = os.path.join(train_dir,'cats')\n",
    "train_dogs_dir = os.path.join(train_dir,'dogs')\n",
    "\n",
    "#验证集\n",
    "val_cats_dir = os.path.join(val_dir,'cats')\n",
    "val_dogs_dir = os.path.join(val_dir,'dogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1996 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#构建数据生成器\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                  rotation_range = 30,\n",
    "                                  width_shift_range = 0.2,\n",
    "                                  height_shift_range = 0.2,\n",
    "                                  shear_range = 0.2,\n",
    "                                  zoom_range = 1.5,\n",
    "                                  horizontal_flip = True,\n",
    "                                  vertical_flip = True)\n",
    "val_datagen = ImageDataGenerator(rescale = 1./255)#训练集和验证机要做同样的预处理\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                   target_size = (75,75),#指定resize的大小\n",
    "                                                   batch_size = 50,\n",
    "                                                   class_mode = 'binary')\n",
    "\n",
    "val_generator = val_datagen.flow_from_directory(val_dir,\n",
    "                                                   target_size = (75,75),\n",
    "                                                   batch_size = 50,\n",
    "                                                   class_mode = 'binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    }
   ],
   "source": [
    "modelcheckpoint = tf.keras.callbacks.ModelCheckpoint('best_xception_modle.h5',\n",
    "                                                             monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=True, mode='auto', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建一个完整的模型，自己制定全连接层，与预训练模型的卷积层组合起来。\n",
    "\n",
    "x = layers.Flatten()(pred_trained_model.output)\n",
    "x = layers.Dense(1024,activation = 'relu')(x)\n",
    "x = layers.Dropout(0.2)(x)\n",
    "\n",
    "x = layers.Dense(1,activation = 'sigmoid')(x)\n",
    "\n",
    "model = Model(pred_trained_model.input,x)\n",
    "\n",
    "model.compile(loss = 'binary_crossentropy',optimizer = 'adam',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-11-c1888589b05d>:6: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "Epoch 1/30\n",
      "40/40 - 51s - loss: 3.1050 - acc: 0.5090 - val_loss: 0.6883 - val_acc: 0.5000\n",
      "Epoch 2/30\n",
      "40/40 - 51s - loss: 0.6855 - acc: 0.5180 - val_loss: 0.6012 - val_acc: 0.7170\n",
      "Epoch 3/30\n",
      "40/40 - 51s - loss: 0.6861 - acc: 0.5230 - val_loss: 0.5517 - val_acc: 0.7270\n",
      "Epoch 4/30\n",
      "40/40 - 51s - loss: 0.6846 - acc: 0.5271 - val_loss: 0.6384 - val_acc: 0.5470\n",
      "Epoch 5/30\n",
      "40/40 - 51s - loss: 0.6752 - acc: 0.5792 - val_loss: 0.4944 - val_acc: 0.7690\n",
      "Epoch 6/30\n",
      "40/40 - 50s - loss: 0.6518 - acc: 0.6087 - val_loss: 0.4510 - val_acc: 0.7850\n",
      "Epoch 7/30\n",
      "40/40 - 45s - loss: 0.6559 - acc: 0.5887 - val_loss: 0.4724 - val_acc: 0.7870\n",
      "Epoch 8/30\n",
      "40/40 - 51s - loss: 0.6407 - acc: 0.5967 - val_loss: 0.4445 - val_acc: 0.8060\n",
      "Epoch 9/30\n",
      "40/40 - 51s - loss: 0.6453 - acc: 0.6017 - val_loss: 0.4671 - val_acc: 0.7680\n",
      "Epoch 10/30\n",
      "40/40 - 51s - loss: 0.6465 - acc: 0.6017 - val_loss: 0.4513 - val_acc: 0.8040\n",
      "Epoch 11/30\n",
      "40/40 - 51s - loss: 0.6463 - acc: 0.6263 - val_loss: 0.4915 - val_acc: 0.7810\n",
      "Epoch 12/30\n",
      "40/40 - 52s - loss: 0.6317 - acc: 0.6318 - val_loss: 0.4438 - val_acc: 0.7900\n",
      "Epoch 13/30\n",
      "40/40 - 51s - loss: 0.6305 - acc: 0.6237 - val_loss: 0.4605 - val_acc: 0.7930\n",
      "Epoch 14/30\n",
      "40/40 - 51s - loss: 0.6144 - acc: 0.6313 - val_loss: 0.4333 - val_acc: 0.7880\n",
      "Epoch 15/30\n",
      "40/40 - 52s - loss: 0.6383 - acc: 0.6087 - val_loss: 0.4536 - val_acc: 0.8060\n",
      "Epoch 16/30\n",
      "40/40 - 52s - loss: 0.6354 - acc: 0.6122 - val_loss: 0.4426 - val_acc: 0.8000\n",
      "Epoch 17/30\n",
      "40/40 - 52s - loss: 0.6161 - acc: 0.6232 - val_loss: 0.4457 - val_acc: 0.7920\n",
      "Epoch 18/30\n",
      "40/40 - 51s - loss: 0.6331 - acc: 0.6222 - val_loss: 0.4259 - val_acc: 0.8000\n",
      "Epoch 19/30\n",
      "40/40 - 49s - loss: 0.6182 - acc: 0.6288 - val_loss: 0.4234 - val_acc: 0.7940\n",
      "Epoch 20/30\n",
      "40/40 - 51s - loss: 0.6392 - acc: 0.6062 - val_loss: 0.4567 - val_acc: 0.7900\n",
      "Epoch 21/30\n",
      "40/40 - 49s - loss: 0.6258 - acc: 0.6212 - val_loss: 0.4520 - val_acc: 0.7820\n",
      "Epoch 22/30\n",
      "40/40 - 47s - loss: 0.6259 - acc: 0.6092 - val_loss: 0.4623 - val_acc: 0.7490\n",
      "Epoch 23/30\n",
      "40/40 - 47s - loss: 0.6223 - acc: 0.6303 - val_loss: 0.4616 - val_acc: 0.7610\n",
      "Epoch 24/30\n",
      "40/40 - 48s - loss: 0.6246 - acc: 0.6107 - val_loss: 0.4469 - val_acc: 0.7960\n",
      "Epoch 25/30\n",
      "40/40 - 51s - loss: 0.6265 - acc: 0.6232 - val_loss: 0.4266 - val_acc: 0.7720\n",
      "Epoch 26/30\n",
      "40/40 - 53s - loss: 0.6294 - acc: 0.6298 - val_loss: 0.4505 - val_acc: 0.7520\n",
      "Epoch 27/30\n",
      "40/40 - 52s - loss: 0.6155 - acc: 0.6373 - val_loss: 0.4408 - val_acc: 0.7800\n",
      "Epoch 28/30\n",
      "40/40 - 52s - loss: 0.6116 - acc: 0.6333 - val_loss: 0.4443 - val_acc: 0.7650\n",
      "Epoch 29/30\n",
      "40/40 - 52s - loss: 0.6132 - acc: 0.6408 - val_loss: 0.4177 - val_acc: 0.7900\n",
      "Epoch 30/30\n",
      "40/40 - 51s - loss: 0.6346 - acc: 0.6137 - val_loss: 0.4404 - val_acc: 0.7820\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(train_generator,\n",
    "                   steps_per_epoch = 40,\n",
    "                   epochs = 30,\n",
    "                   validation_data = val_generator,\n",
    "                   validation_steps = 20,\n",
    "                   verbose = 2,callbacks = [modelcheckpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'the loss of the model')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制模型的训练效果\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "plt.subplot(121)\n",
    "plt.plot(epochs,acc,'r-',label = 'training accuracy')\n",
    "plt.plot(epochs,val_acc,'g-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.title('The accuracy of the model')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(epochs,loss,'r-',label = 'training loss')\n",
    "plt.plot(epochs,val_loss,'g-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.title('the loss of the model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tensorflow库为我们提供了很多预训练模型，这些模型都是ImageNet竞赛的冠军模型。我们采用这些模型（卷积层部分）作为我们的模型参数初始化，我们自己定义全连接层，然后将两部分拼接起来形成一个完整的模型。\n",
    "\n",
    "预训练模型有很多，我们倾向选择轻量化（参数量少），准确率高的模型，像Resnet, Inception_V3, Xception, MobileNet等。VGG不建议，其有一亿三千万个参数，模型非常臃肿，训练起来非常的吃力。\n",
    "\n",
    "通过使用预训练模型，大大简化了训练的难度，而且得到效果也非常的不错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
